Paper published in a book (Scientific congresses and symposiums)
Integrating Domain Adaptation and Causal Discovery in Digital Twins for Plastic Injection Molding
Paldino, Gian Marco; Caelen, Olivier; Oueslati, Maroueneet al.
2025 • In Proceedings - 2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2025
Causal Discovery; Digital Twin; Domain Adaptation; Machine Learning; Plastic Injection Molding; Causal discovery; Domain adaptation; Machine-learning; Manufacturing industries; Material conditions; Optimisations; Plastic injection molding; Predictive maintenance; Production system; Real-time process monitoring; Information Systems; Modeling and Simulation; Safety, Risk, Reliability and Quality; Computer Science Applications; Health Informatics; Computer Networks and Communications; Information Systems and Management; Artificial Intelligence
Abstract :
[en] The development of digital twin (DT) technology is transforming the manufacturing industry, allowing for applications such as real-time process monitoring, predictive maintenance, and optimization of production systems. However, traditional DT frameworks often maintain a high-level perspective and do not consider the challenges arising from real industrial data. Factors such as changes in input materials and environmental conditions can hinder the functioning of DTs and are frequently overlooked. Using the example of the plastic injection molding industry, this paper highlights the necessity of including technologies such as domain adaptation and causal discovery in the development of DTs. Domain adaptation enables adaptation to changes, while causal discovery provides a deeper understanding of the underlying process dynamics. Their combined adoption allows DTs to achieve improved robustness and flexibility, extending their applicability across diverse manufacturing scenarios.
Disciplines :
Computer science
Author, co-author :
Paldino, Gian Marco; Université Libre de Bruxelles, Machine Learning Group, Brussels, Belgium
Caelen, Olivier; Sirris, Belgium
Oueslati, Marouene; Sirris, Belgium
Ansay, Marc; Sirris, Belgium
Andrianandrianinajohanesa, Tojo Valisoa ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle ; Université de Mons - UMONS > Faculté Polytechnique > Service de Génie Mécanique
Integrating Domain Adaptation and Causal Discovery in Digital Twins for Plastic Injection Molding
Publication date :
March 2025
Event name :
2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
Event place :
Washington, Usa
Event date :
17-03-2025 => 21-03-2025
Audience :
International
Main work title :
Proceedings - 2025 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2025
Publisher :
Institute of Electrical and Electronics Engineers Inc.
Y. Lu, C. Liu, I. Kevin, K. Wang, H. Huang, and X. Xu, "Digital twindriven smart manufacturing: Connotation, reference model, applications and research issues," Robotics and computer-integrated manufacturing, vol. 61, p. 101837, 2020.
G. M. Paldino, F. De Caro, J. De Stefani, A. Vaccaro, D. Villacci, and G. Bontempi, "A digital twin approach for improving estimation accuracy in dynamic thermal rating of transmission lines," Energies, vol. 15, no. 6, p. 2254, 2022.
P. Bibow, M. Dalibor, C. Hopmann, B. Mainz, B. Rumpe, D. Schmalzing, M. Schmitz, and A. Wortmann, "Model-driven development of a digital twin for injection molding," in International Conference on Advanced Information Systems Engineering. Springer, 2020, pp. 85-100.
F. Tao and M. Zhang, "Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing," IEEE access, vol. 5, pp. 20 418-20 427, 2017.
G. M. Paldino, F. De Caro, J. De Stefani, A. Vaccaro, and G. Bontempi, "Transfer learning-based methodologies for dynamic thermal rating of transmission lines," Electric Power Systems Research, vol. 229, p. 110206, 2024.
M. Lippi, M. Martinelli, M. Picone, and F. Zambonelli, "Enabling causality learning in smart factories with hierarchical digital twins," Computers in Industry, vol. 148, p. 103892, 2023.
B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio, "Toward causal representation learning," Proceedings of the IEEE, vol. 109, no. 5, pp. 612-634, 2021.
A. Rehmer, M. Klute, H.-P. Heim, and A. Kroll, "A digital twin for part quality prediction and control in plastic injection molding," in Modeling, Identification, and Control for Cyber-Physical Systems Towards Industry 4.0. Elsevier, 2024, pp. 79-109.
G. E. Modoni, B. Stampone, and G. Trotta, "Application of the digital twin for in process monitoring of the micro injection moulding process quality," Computers in Industry, vol. 135, p. 103568, 2022.
F. J. Lacueva-Pérez, S. Hermawati, P. Amoraga, R. Salillas-Martínez, R. del Hoyo-Alonso, and G. Lawson, "Shion (smart thermoplastic injection): An interactive digital twin supporting real-time shopfloor operations," IEEE Internet Computing, vol. 26, no. 3, pp. 23-32, 2020.
Z. Wang, W. Feng, J. Ye, J. Yang, and C. Liu, "A study on intelligent manufacturing industrial internet for injection molding industry based on digital twin," Complexity, vol. 2021, no. 1, p. 8838914, 2021.
S. Nasiri, M. R. Khosravani, T. Reinicke, and J. Ovtcharova, "Digital twin modeling for smart injection molding," Journal of Manufacturing and Materials Processing, vol. 8, no. 3, p. 102, 2024.
X. Zheng, B. Aragam, P. K. Ravikumar, and E. P. Xing, "Dags with no tears: Continuous optimization for structure learning," Advances in neural information processing systems, vol. 31, 2018.
D. Koller, "Probabilistic graphical models: Principles and techniques," 2009.
M. Kaiser and M. Sipos, "Unsuitability of notears for causal graph discovery when dealing with dimensional quantities," Neural Processing Letters, vol. 54, no. 3, pp. 1587-1595, 2022.
G. Bontempi and M. Flauder, "From dependency to causality: A machine learning approach." J. Mach. Learn. Res., vol. 16, no. 1, pp. 2437-2457, 2015.
G. Bontempi, "Learning causal dependencies in large-variate time series," in 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020, pp. 1-7.
